DBMS > Amazon Redshift vs. Apache Druid vs. Google Cloud Spanner vs. Oracle

System Properties Comparison Amazon Redshift vs. Apache Druid vs. Google Cloud Spanner vs. Oracle

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Widely used RDBMS

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

aws.amazon.com/redshift

druid.apache.org

cloud.google.com/spanner

www.oracle.com/database

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

cloud.google.com/spanner/docs

docs.oracle.com/en/database

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Google

Oracle

Initial release

2012

2017

1980

Current release

29.0.1, April 2024

23c, September 2023

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Server operating systems

hosted

Linux
OS X
Unix

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

yes

schema-less columns are supported

yes

Schemaless in JSON and XML columns

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

yes

Query statements complying to ANSI 2011

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in Python

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Strict serializable isolation

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

fine grained access rights according to SQL-standard

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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